CN116867075B - Channel allocation method in cellular and D2D user clustering network - Google Patents
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Abstract
The invention discloses a channel allocation method in a cellular and D2D user clustering network, which comprises the following steps of S1, modeling a base station position by using a poisson point process, and modeling the D2D user and the cellular user position by using a poisson clustering process to form a clustering network. S2, after the model is built, assuming that the total number of cellular users is M and the total number of D2D users is N, the cellular users are expressed as a set C= { C 1 ,C 2 ,…,C i ,C M (D2D user is represented as a set d= { D) 1 ,D 2 ,…,D j ,D N ,}. S3, calculating the signal-to-noise ratio of the cellular user and the D2D user respectively, and setting minimum signal-to-noise ratio thresholds required by the cellular user and the D2D user. And S4, respectively calculating the channel capacities of the cellular user and the D2D user to obtain the system capacity in the clustered network. And S5, optimally matching the D2D users in each cluster with available cellular user resources by using a KM algorithm and maximizing the system capacity of the clustered network.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a channel allocation method in a cellular and D2D user clustering network.
Background
D2D communication is a new technology proposed to cope with the increase in the number of intelligent terminals and the explosive increase in the network communication capacity demand. The D2D communication can improve the bandwidth utilization rate of the network and relieve the problem of insufficient bandwidth of the current cellular network. The D2D communication technology improves the transmission speed of the network, expands the coverage of the network, and reduces the power consumption of the terminal. Under the condition that the distance between two terminal devices is close, the D2D communication can keep the channel quality of the devices better, and the data transmission can be directly realized without the cooperation of base stations.
Since D2D communication can perform communication within a certain range independently of control of a base station, in a practical environment, communication environment is often deteriorated due to the influence of various obstacles, and the overall performance of the system is deteriorated. For example, if the distance between two D2D users is too large, or the line-of-sight path is blocked by an obstacle such as a tall building, the transmission is likely to fail. Often in practical applications, the D2D user may achieve the communication purpose by multiplexing the channel resources of the cellular user.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art, and provides a channel allocation method in a cellular and D2D user clustering network, which has the following technical scheme:
a channel allocation method in a cellular and D2D user clustering network. Considering that the coexistence of D2D users and cellular users in a certain area is likely to form a clustered communication network, co-channel interference in the D2D network becomes a key issue that cannot be ignored. Considering that the locations of base stations and users in a real scenario are often clustered together to form a clustered network, the locations of Base Stations (BS) are modeled using a poisson process (PPP), and the D2D user and cellular user locations are modeled using a Poisson Clustering Process (PCP). It is assumed that the base station can obtain complete Channel State Information (CSI) for all users in the clustered network. Each cellular user occupies a separate orthogonal channel for communication with the base station, which means that there is no interference between the cellular users. Allowing bandwidth resources of one cellular user to be reused by multiple D2D users. Thus, the interference of a cellular user is caused by multiple D2D users occupying their channels. Each D2D user can only communicate using the channel of one cellular user. The interference of D2D users is caused by the D2D users multiplexing cellular user channels. In order to solve the interference problem, the invention utilizes the KM algorithm to allocate the cellular user channel for the D2D users in the clustered network, thereby realizing the optimal matching of the cellular users and the D2D users.
A method of channel allocation in a cellular and D2D user clustered network, comprising:
s1, modeling the position of a base station by using a Poisson Point Process (PPP), and modeling the positions of a D2D user and a cellular user by using a Poisson Clustering Process (PCP) to form a clustering network.
S2, after the model is built, assuming that the total number of cellular users is M and the total number of D2D users is N, the cellular users are expressed as a set C= { C 1 ,C 2 ,…,C i ,C M (D2D user is represented as a set d= { D) 1 ,D 2 ,…,D j ,D N ,}。
S3, calculating the signal-to-noise ratio of the cellular user and the D2D user respectively, and setting minimum signal-to-noise ratio thresholds required by the cellular user and the D2D user.
And S4, respectively calculating the channel capacities of the cellular user and the D2D user, and obtaining the total system capacity in the clustered network.
And S5, optimally matching the D2D users in each cluster with available cellular user resources by using a KM algorithm, and maximizing the sum rate of the clustered networks.
Further, in the S1, the location of the base station is used with a density λ c Is drawn by PPP of (2), the position of the base station is represented by x epsilon phi c Represent phi c The location denoted as base station is modeled as a poisson process. Cellular Users (CUEs) and D2D Users (DUEs) are distributed around each base station, modeled as PCPs. Thus, the base station and the user will be referred to as a cluster center and cluster members, respectively. Each cluster center x epsilon phi c The surrounding cluster members are sampled from the independent same distribution (i.i.d) symmetrical normal distribution, and the variance of CUEs isThe variance of DUEs is +.>Thus, the Probability Density Function (PDF) of CUEs belonging to a location of an x-centered cluster member relative to its cluster center location can be expressed as:
wherein y is c Representing the distance of the CUE relative to the cluster center.
Likewise, the Probability Density Function (PDF) of the locations of DUEs belonging to an x-centered cluster member relative to its cluster center location can be expressed as:
wherein y is d Representing the distance of the CUE relative to the cluster center.
Further, in the S2, assuming that the total number of cellular users is M and the total number of D2D users is N, the cellular users are represented as one set c= { C 1 ,C 2 ,…,C i ,C M (wherein C) i Denoted as the ith cellular user, the D2D user is denoted as a set d= { D 1 ,D 2 ,…,D j ,D N (D), where D j Denoted as the j-th D2D user,
further, in said S3, a typical cellular subscriber CU i The reception of SIR on the kth channel is expressed as:
wherein P is C For the transmit power of the cellular user on each sub-channel,the transmit power on the kth subchannel for the jth D2D pair. />For the fading coefficients of the rayleigh channel, α is the path loss index, and α > 2./>For clustering the distances of typical cellular users CU to BS in the network, +.>To cluster the distance of a typical D2D Transmitter (DT) to BS in a network, x i,j Assigning index variable, x, to a channel i,j E {0,1}. When x is i,j When=1, the j-th D2D pair is represented as occupying the channel of the cellular user i, and is the opposite x i,j When=0, the channel of cellular user i is unoccupied.
With CU i The SIR for a j-th pair of typical D2D users on the k-th channel is similar, expressed as:
wherein P is C Is beeThe transmit power of the cellular users on each sub-channel, L is the distance between the D2D transmitter and the D2D receiver,is the transmission power of the f-th pair of D2D users on the kth sub-channel, +.>Is the fading coefficient, h, between DT and D2D Receiver (DR) links in a typical clustered network i,j Is the channel gain of the ith cellular user channel shared by j pairs of D2D users, h f,j Is the channel gain when the f-th pair of D2D users shares the same resources as the j-th pair of D2D users.
Respectively, gamma is treated c And gamma d Is set to the minimum SIR threshold required for cellular users and D2D users. Typical cellular subscriber CU i The reception of SIR on the kth channel and the reception of SIR on the kth channel for the j-th pair of typical D2D users satisfy the following condition:
the specific meanings are as follows:
S3.1，CU is subject to interference from BSs within other clustered networks for typical cellular users, +.>The formula is as follows:
wherein,is the distance of a typical CU from BSs within other clustered networks.
S3.2，For inter-cluster network interference from D2D Transmitters (DTs), +.>The formula is as follows:
wherein,representing fading coefficients between a typical CU and DT links in other clustered networks, +.>Representing the distance of a typical CU to DTs within other clustered networks.
S3.3，For intra-cluster interference from DTs, +.>The formula is as follows:
wherein,representing the fading coefficient between a typical CU and DT link within a clustered network,/>Representing the distance between a typical CU and DT within the clustered network.
S3.4，Is inter-cluster-network interference from BS, +.>The formula is as follows:
wherein,is the distance of a typical DR from BSs within other clustered networks.
S3.5，Is interference between clustered networks from DTs, < >>The formula is as follows:
wherein,representing the link fading coefficient between typical DR and DT in other clustered networks, +.>Is the distance between the two.
S3.6，Is from BSInterference within a class network,/->The formula is as follows:
wherein,is the distance of DR from BS in a typical clustered network.
Further, in the S4, the capacities of the ith cellular user and the jth pair of D2D users are denoted as C, respectively c,i And C d,j ：
Then, the total system capacity C in a clustered network sum The method comprises the following steps:
further, in S5, since the D2D users in the same cluster are close in distance, interference may be caused to each other, the same resources cannot be used, but the D2D users of different clusters may share the same resources. Considering that D2D users in each D2D user cluster can only communicate using channel resources of one cellular user, a KM algorithm can be used to optimally match D2D users in each cluster with available cellular user resources and maximize the system capacity of the clustered network.
The KM algorithm flow is specifically as follows:
and performing bipartite graph matching on the D2D user and the cellular user. ClusterThe D2D user set and the cellular user set in (a) are represented as two non-intersecting sets of vertex sets in the bipartite graph, respectively. Each D2D user selects the most appropriate occupation from among cellular users satisfying the SIR threshold condition, i.eIf and only if the cellular subscriber is multiplexed by the D2D subscriber, a connection line is established between the two subscribers, the weight value on the connection line is W j,i I.e. the sum of the capacity of the jth D2D user after occupying the ith cellular user channel.
The invention has the beneficial effects that:
according to the invention, the coexistence situation of the cellular user and the D2D user in the clustered network is considered, and compared with the traditional communication model, the model accuracy is higher, and the communication efficiency can be remarkably improved. And meanwhile, optimal channel allocation is carried out on the cellular user and the D2D user by using a KM algorithm. The system capacity is effectively improved while the communication quality of the user is ensured.
Drawings
FIG. 1 is a structural model diagram of a channel allocation method in a cellular and D2D user clustering network;
fig. 2 is a diagram of optimal channel allocation using KM calculation in the present solution;
FIG. 3 is a system flow diagram of a channel allocation method in a cellular and D2D user clustered network;
Detailed Description
The invention is further described below with reference to the accompanying drawings.
A structural model diagram of a channel allocation method in a cellular and D2D user clustering network, as shown in fig. 1, and specific steps are shown in fig. 3, including the following steps:
step S1, modeling the base station position by using a Poisson Point Process (PPP), and modeling the D2D user position and the cellular user position by using a Poisson Clustering Process (PCP) to form a clustering network. The location of the base station is used with a density lambda c Is drawn by PPP of (2), the position of the base station is represented by x epsilon phi c Represent phi c The location denoted as base station is modeled as a poisson process. Cellular subscriber(CUEs) and D2D Users (DUEs) are distributed around each base station, modeled as PCPs. Thus, the base station and the user will be referred to as a cluster center and cluster members, respectively. Each cluster center x epsilon phi c The surrounding cluster members are sampled from the independent same distribution (i.i.d) symmetrical normal distribution, and the variance of CUEs isThe variance of DUEs is +.>Thus, the Probability Density Function (PDF) of a location belonging to an x-centered cluster member with respect to its cluster center location CUEs can be expressed as:
wherein y is c Representing the distance of the CUE relative to the cluster center.
Likewise, the Probability Density Function (PDF) of the locations of DUEs belonging to an x-centered cluster member relative to its cluster center location can be expressed as:
wherein y is d Representing the distance of the CUE relative to the cluster center.
In step S2, we represent the cellular users as a set c= { C, assuming the total number of cellular users is M and the total number of D2D users is N 1 ,C 2 ,…,C i ,C M (wherein C) i Denoted as the ith cellular user, we denote D2D user as a set d= { D 1 ,D 2 ,…,D j ,D N (D), where D j Denoted as j-th D2D user, the transmit power of the cellular user on each subchannel is P C Representing the j-th D2D pair for the transmit power on the k-th sub-channelAnd (3) representing.
Step S3, typical cellular subscriber CU i The reception of SIR on the kth channel is expressed as:
wherein, P C For the transmit power of the cellular user on each sub-channel,the transmit power on the kth subchannel for the jth D2D pair. />For the fading coefficients of the rayleigh channel, α is the path loss index, and α > 2./>For clustering the distances of typical CUs to BSs in the network, +.>To cluster the distance of a typical D2D Transmitter (DT) to BS in a network, x i,j Assigning index variable, x, to a channel i,j E {0,1}. When x is i,j When=1, the j-th D2D pair is represented as occupying the channel of the cellular user i, and is the opposite x i,j When=0, the channel of cellular user i is unoccupied.
With CU i The SIR for a j-th pair of typical D2D users on the k-th channel is similar, expressed as:
wherein P is C Is the transmit power of the cellular user on each sub-channel, L is the distance between the D2D transmitter and the D2D receiver,is the transmission power of the f-th pair of D2D users on the kth sub-channel, +.>Is the fading coefficient, h, between DT and D2D Receiver (DR) links in a typical clustered network i,j Is the channel gain of the ith cellular user channel shared by j pairs of D2D users, h l,j Is the channel gain when the f-th pair of D2D users shares the same resources as the j-th pair of D2D users.
Respectively, gamma is treated c And gamma d Is set to the minimum SIR threshold required for cellular users and D2D users. Typical cellular subscriber CU i The reception of SIR on the kth channel and the reception of SIR on the kth channel for the j-th pair of typical D2D users satisfy the following condition:
step S3.1 willIndicated as typical cellular subscriber CUs are subject to interference from BSs within other clustered networks,the formula is as follows:
wherein,is the distance of a typical CU from BSs within other clustered networks.
Step S3.2 willExpressed as interference between clustered networks from D2D Transmitters (DTs), +.>The formula is as follows:
wherein,representing fading coefficients between a typical CU and DT links in other clustered networks, +.>Representing the distance of a typical CU to DTs within other clustered networks.
Step S3.3 willIntra-cluster interference, denoted as interference from DTs, < >>The formula is as follows:
wherein,representing the fading coefficient between a typical CU and DT link within a clustered network,/>Representing the distance between a typical CU and DT within the clustered network.
Step S3.4 willExpressed as interference between clustered networks from BS,/->The formula is as follows:
wherein,is the distance of a typical DR from BSs within other clustered networks.
Step S3.5 willExpressed as interference between clustered networks from DTs,/->The formula is as follows:
wherein,representing the link fading coefficient between typical DR and DT in other clustered networks, +.>Is the distance between the two.
Step S3.6 willDenoted interference within clustered network from BS,/->The formula is as follows:
wherein,is the distance of DR from BS in a typical clustered network.
Step S4, according to shannon theory, the capacities of the ith cellular user and the jth pair of D2D users are respectively expressed as:
the sum rate in a clustered network is then:
in step S5, as shown in fig. 2, since D2D users in the same cluster are close in distance, interference is caused to each other, and the same resources cannot be used, but D2D users in different clusters may share the same resources. Considering that D2D users in each D2D user cluster can only communicate using channel resources of one cellular user, a KM algorithm can be used to optimally match D2D users in each cluster with available cellular user resources.
The KM algorithm flow is specifically as follows:
and performing bipartite graph matching on the D2D user and the cellular user. The D2D user set and the cellular user set in the cluster are represented as two non-intersecting sets of vertex sets in the bipartite graph, respectively. Each D2D user selects the most appropriate occupation from among cellular users satisfying the SIR threshold condition, i.eIf and only if the cellular subscriber is multiplexed by the D2D subscriber, a connection line is established between the two subscribers, the weight value on the connection line is W j,i I.e. the sum of the capacity of the jth D2D user after occupying the ith cellular user channel.
The invention considers the coexistence situation of the cellular user and the D2D user in the clustering network, and compared with the traditional communication model, the model of the invention has the advantages of no loss of generality, high precision and high communication efficiency. And meanwhile, optimal channel allocation is carried out on the cellular user and the D2D user by using a KM algorithm. The system capacity is effectively improved while the communication quality of the user is ensured.
The above list of detailed descriptions is only specific to practical embodiments of the present invention, and they are not intended to limit the scope of the present invention, and all equivalent manners or modifications that do not depart from the technical scope of the present invention should be included in the scope of the present invention.
Claims (1)
1. A method for channel allocation in a cellular and D2D user clustered network, comprising the steps of:
s1, modeling a base station position by using a Poisson Point Process (PPP), and modeling D2D user and cellular user positions by using a Poisson Clustering Process (PCP) to form a clustering network;
s2, after the model is built, assuming that the total number of cellular users is M and the total number of D2D users is N, the cellular users are expressed as a set C= { C 1 ,C 2 ,…,C i ,C M (D2D user is represented as a set d= { D) 1 ,D 2 ,…,D j ,D N ,}；
S3, calculating signal-to-noise ratios of the cellular user and the D2D user respectively, and setting minimum signal-to-noise ratio thresholds required by the cellular user and the D2D user;
s4, respectively calculating the channel capacities of the cellular user and the D2D user, and obtaining the total system capacity in the clustered network
S5, optimally matching the D2D users in each cluster with available cellular user resources by using a KM algorithm and maximizing the sum rate of the cluster network;
the S1 specifically comprises the following steps:
the location of the base station is used with a density lambda c Is drawn by PPP of (2), the position of the base station is represented by x epsilon phi c Represent phi c The location denoted as base station is modeled as poisson point process, cellular Users (CUEs) and D2D Users (DUEs) are distributed around each base station, modeled as PCPs, the base station and users will be referred to as cluster centers and cluster members, respectively, each cluster center x e phi c The surrounding cluster members are sampled from the independent same distribution (i.i.d) symmetrical normal distribution, and the variance of CUEs isThe variance of DUEs is +.>Thus, the Probability Density Function (PDF) of a location belonging to an x-centered cluster member relative to its cluster center location dus can be expressed as:
wherein y is c Representing the distance of the CUE relative to the cluster center;
likewise, the Probability Density Function (PDF) of the locations of DUEs belonging to an x-centered cluster member relative to its cluster center location can be expressed as:
wherein y is d Representing the distance of the CUE relative to the cluster center;
the step S2 is specifically as follows:
assuming that the total number of cellular users is M and the total number of D2D users is N, the cellular users are represented as one set c= { C 1 ,C 2 ,…,C i ,C M (wherein C) i Denoted as the ith cellular user, the D2D user is denoted as a set d= { D 1 ,D 2 ,…,D j ,D N (D), where D j Denoted as j-th D2D user, the transmit power of the cellular user on each subchannel is P C Representing the j-th D2D pair for the transmit power on the k-th sub-channelA representation;
the signal to noise ratio of the cellular user in S3 is calculated as follows:
typical cellular subscriber CU i The reception of SIR on the kth channel is expressed as:
wherein, P C For the transmit power of the cellular user on each sub-channel,for the j-th D2D pair the transmit power on the k-th sub-channel, +.>For the fading coefficients of the Rayleigh channel, α is the path loss index, and α > 2,/is>For clustering the distances of typical CUs to BSs in the network, +.>To cluster the distance of a typical D2D Transmitter (DT) to BS in a network, x i,j Assigning index variable, x, to a channel i,j E {0,1}, when x i,j When=1, the j-th D2D pair is represented as occupying the channel of the cellular user i, and is the opposite x i,j When=0, the channel of cellular user iUnoccupied;
the signal to noise ratio of the D2D user in S3 is calculated as follows:
the reception of SIR on the kth channel for the jth pair of typical D2D users is expressed as:
wherein P is C Is the transmit power of the cellular user on each sub-channel, L is the distance between the D2D transmitter and the D2D receiver,is the transmission power of the f-th pair of D2D users on the kth sub-channel, +.>Is the fading coefficient, h, between DT and D2D Receiver (DR) links in a typical clustered network l,j Is the channel gain when the f-th pair of D2D users and the j-th pair of D2D users share the same resource;
CU is subject to interference from BSs within other clustered networks for typical cellular users, +.>The formula is as follows:
wherein,distance from the BS in other clustered networks for a typical CU;
for inter-cluster network interference from D2D Transmitters (DTs), +.>The formula is as follows:
wherein,representing fading coefficients between a typical CU and DT links in other clustered networks, +.>Representing the distance of a typical CU to DT within other clustered networks;
for intra-cluster interference from DTs, +.>The formula is as follows:
wherein,representing the fading coefficient between a typical CU and DT link within a clustered network,/>Representing the distance between a typical CU and DT within the clustered network;
is inter-cluster-network interference from BS, +.>The formula is as follows:
wherein,distance from BSs within other clustered networks for a typical DR;
is interference between clustered networks from DTs, < >>The formula is as follows:
wherein,representing the link fading coefficient between typical DR and DT in other clustered networks, +.>Is the distance between the two;
is interference within the clustered network from BS, +.>The formula is as follows:
wherein,is the distance between DR and BS in a typical clustered network;
in the S4, the capacities of the ith cellular user and the jth pair of D2D users are expressed as:
in S4, the total system capacity in one clustered network is:
in the S3, gamma is as follows c And gamma d Set to minimum SIR threshold required for cellular and D2D users, typical cellular user CU i The reception of SIR on the kth channel and the reception of SIR on the kth channel for the j-th pair of typical D2D users satisfy the following condition:
in the step S5, the specific process of optimally matching the D2D users in each cluster with the available cellular user resources and maximizing the sum rate of the clustered network using KM algorithm is as follows:
performing bipartite graph matching on the D2D user and the cellular user; the D2D user set and the cellular user set in the cluster are represented as two non-intersecting sets of vertex sets in the two figures, respectively, each D2D user selecting the most suitable from the cellular users satisfying the SIR threshold condition for occupation, i.eIf and only if the cellular subscriber is multiplexed by the D2D subscriber, a connection line is established between the two subscribers, the weight value on the connection line is W j,i I.e. the sum of the capacity of the jth D2D user after occupying the ith cellular user channel.
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| CN107277819A (en) * | 2017-06-23 | 2017-10-20 | 电子科技大学 | A kind of frequency spectrum resource division methods based on heterogeneous network |
| CN107484244A (en) * | 2017-08-01 | 2017-12-15 | 东南大学 | The cellular network D2D communication spectrum resource allocation algorithms that a kind of QoS is perceived |
| CN107612745A (en) * | 2017-10-12 | 2018-01-19 | 北京科技大学 | A kind of method of determination D2D network models and the method for assessing D2D network model performances |
| CN111586646A (en) * | 2020-05-27 | 2020-08-25 | 重庆大学 | Resource allocation method for D2D communication combining uplink and downlink channels in cellular network |
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